Command A vs GLM 5
GLM 5 leads Command A on the Artificial Analysis Intelligence Index, 27.9 to 13.1, and GLM 5 costs 3.1x less per output token ($3.20 vs $10.00 per million). Updated October 22, 2026; 46 benchmarks have a result for both.
Command A or GLM 5: which to pick
Only GLM 5 has a coding score, 41.7 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding.
Pick Command A for a larger context window (256K against 205K tokens) and work measured by CaseLaw (Vals AI) or IFBench, where it leads.
- Shared results
- 46
- Command A only
- 16
- GLM 5 only
- 30
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only GLM 5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5
GLM 5 leads, 27.9 against 13.1.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Command A has a AA Agentic Index result, 1.0.
Directional only
Reasoning
GPQA Diamond
GLM 5
GLM 5 leads, 83.3% against 48.5%.
Same board, both measured
Math
AIME
GLM 5
GLM 5 leads, 91.7% against 13.3%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5
GLM 5 leads, 86.0% against 69.2%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 5 has a Design Arena: websites result, 1247.
Directional only
Long documents
Listed context window
Command A
Command A takes 256K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5
Command A $0.0075, GLM 5 $0.0026 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5
Command A $2.69, GLM 5 $0.63.
Published attempt cost and pass rate
At a glance
- Coding index
- Command A not benchmarked, GLM 5 41.7 from 3 benchmarks.
- Widest category
- Math, 41.6 points apart on average over 3 shared benchmarks.
- Widest single gap
- AIME: Command A 13.3% against GLM 5 91.7%, 78.3 points apart on a 0 to 100 scale.
- Output price
- Command A $10.00 against GLM 5 $3.20 per million output tokens, 3.1x apart.
- Context window
- Command A takes 256K tokens, GLM 5 205K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Command A | Bars | GLM 5 | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 7 | ||
| Math | 0 | 3 | ||
| Knowledge | 1 | 9 | ||
| General | 1 | 18 | ||
| All shared boards | 2 | 44 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
| Benchmark | Command A | GLM 5 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 72.8% | Only GLM 5 measured |
| SWE-bench Multilingual | Not measured | 69.7% | Only GLM 5 measured |
| LMArena WebDev | Not measured | 1435 | Only GLM 5 measured |
| AA Coding Index | 27.8 | Not measured | Only Command A measured |
| LiveCodeBench (Vals AI) | 35.1% | 81.9% | GLM 5 |
| SWE-bench Verified (Vals AI) | 7.8% | 71.4% | GLM 5 |
| Vibe Code Bench (Vals AI) | Not measured | 23.4% | Only GLM 5 measured |
| Terminal-Bench 2.1 (Vals AI) | 17.6% | Not measured | Only Command A measured |
| LiveCodeBench (Artificial Analysis run) | 28.7% | Not measured | Only Command A measured |
| SciCode | 38.5% | Not measured | Only Command A measured |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | 43.2% | GLM 5 |
| Terminal-Bench | Not measured | 52.4% | Only GLM 5 measured |
| WeirdML | Not measured | 48.2% | Only GLM 5 measured |
| ALE-Bench | Not measured | 765.6 | Only GLM 5 measured |
| LMArena Text: coding | 1330 | 1461 | GLM 5 |
| LMArena Text: software and IT | 1336 | 1460 | GLM 5 |
| LMArena WebDev: React | Not measured | 1424 | Only GLM 5 measured |
| LMArena WebDev: HTML | Not measured | 1450 | Only GLM 5 measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Command A | GLM 5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 27.9 | GLM 5 |
| Epoch Capabilities Index | Not measured | 145.8 | Only GLM 5 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Command A | GLM 5 | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | Not measured | Only Command A measured |
| Design Arena: agents building full-stack apps | Not measured | 1123 | Only GLM 5 measured |
| Design Arena: agents building mobile apps | Not measured | 1136 | Only GLM 5 measured |
| Design Arena: agents building Android apps | Not measured | 1147 | Only GLM 5 measured |
| Design Arena: agents building Godot games | Not measured | 1144 | Only GLM 5 measured |
| Design Arena: HTML slides | Not measured | 1098 | Only GLM 5 measured |
| Finance Agent (Vals AI) | 9.0% | Not measured | Only Command A measured |
| τ²-Bench Telecom | 80.7% | 98.2% | GLM 5 |
| Vending-Bench 2 | Not measured | $4,432 | Only GLM 5 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 46.5% | Not measured | Only Command A measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | Command A | GLM 5 | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | 83.3% | GLM 5 |
| GPQA Diamond (Artificial Analysis run) | 76.1% | 82.0% | GLM 5 |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | 29.3% | GLM 5 |
| GPQA Diamond (Epoch AI run) | Not measured | 87.8% | Only GLM 5 measured |
| ARC-AGI | Not measured | 44.7% | Only GLM 5 measured |
| ARC-AGI-2 | Not measured | 4.9% | Only GLM 5 measured |
| SimpleBench | Not measured | 53.2% | Only GLM 5 measured |
| LMArena Text: hard prompts | 1326 | 1452 | GLM 5 |
| LMArena Text: expert | 1296 | 1454 | GLM 5 |
| LMArena Text: hard prompts in English | 1335 | 1460 | GLM 5 |
| Kagi LLM Benchmark | 28.8% | 75.0% | GLM 5 |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Command A | GLM 5 | Leader |
|---|---|---|---|
| AIME | 13.3% | 91.7% | GLM 5 |
| MATH 500 (Vals AI) | 76.2% | Not measured | Only Command A measured |
| MGSM (Vals AI) | 85.7% | Not measured | Only Command A measured |
| AA Math Index | 13.0 | Not measured | Only Command A measured |
| AIME 2025 (Artificial Analysis run) | 13.0% | Not measured | Only Command A measured |
| OTIS Mock AIME | Not measured | 80.0% | Only GLM 5 measured |
| LMArena Text: math | 1300 | 1440 | GLM 5 |
| LMArena Text: mathematical work | 1312 | 1451 | GLM 5 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index19
| Benchmark | Command A | GLM 5 | Leader |
|---|---|---|---|
| MMLU Pro | 69.2% | 86.0% | GLM 5 |
| LegalBench (Vals AI) | 79.7% | 84.1% | GLM 5 |
| CaseLaw (Vals AI) | 64.5% | 52.5% | Command A |
| CorpFin (Vals AI) | 46.0% | 62.9% | GLM 5 |
| TaxEval (Vals AI) | 61.4% | 70.0% | GLM 5 |
| MedQA (Vals AI) | 80.5% | 94.3% | GLM 5 |
| MedCode (Vals AI) | 19.7% | Not measured | Only Command A measured |
| MMLU Pro (Artificial Analysis run) | 71.2% | Not measured | Only Command A measured |
| LMArena Text: science | 1339 | 1460 | GLM 5 |
| LMArena Text: medicine and healthcare | 1334 | 1455 | GLM 5 |
| LMArena Text: legal and government | 1348 | 1453 | GLM 5 |
| LMArena Text: business and finance | 1315 | 1430 | GLM 5 |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Command A | GLM 5 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1247 | Only GLM 5 measured |
| Design Arena: UI components | Not measured | 1227 | Only GLM 5 measured |
| Design Arena: game dev | Not measured | 1234 | Only GLM 5 measured |
| Design Arena: data viz | Not measured | 1228 | Only GLM 5 measured |
| Design Arena: 3D | Not measured | 1228 | Only GLM 5 measured |
| Design Arena: all code categories | Not measured | 1246 | Only GLM 5 measured |
| Design Arena: SVG | Not measured | 1168 | Only GLM 5 measured |
| Design Arena: ASCII art | Not measured | 1147 | Only GLM 5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index118
| Benchmark | Command A | GLM 5 | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 55.7% | Not measured | Only Command A measured |
| IFBench | 73.9% | 72.3% | Command A |
| Long context reasoning (AA-LCR) | 52.7% | 75.7% | GLM 5 |
| DTBench | 61.3% | Not measured | Only Command A measured |
| LMCA | 10.3% | Not measured | Only Command A measured |
| CL-bench | Not measured | 18.7% | Only GLM 5 measured |
| LMArena Text | 1331 | 1446 | GLM 5 |
| LMArena Text: instruction following | 1309 | 1428 | GLM 5 |
| LMArena Text: creative writing | 1319 | 1438 | GLM 5 |
| LMArena Text: English | 1344 | 1458 | GLM 5 |
| LMArena Text: non-English | 1313 | 1430 | GLM 5 |
| LMArena Text: Chinese | 1327 | 1511 | GLM 5 |
| LMArena Text: German | 1342 | 1447 | GLM 5 |
| LMArena Text: Japanese | 1285 | 1416 | GLM 5 |
| LMArena Text: Korean | 1285 | 1423 | GLM 5 |
| LMArena Text: French | 1351 | 1456 | GLM 5 |
| LMArena Text: Spanish | 1347 | 1454 | GLM 5 |
| LMArena Text: Russian | 1314 | 1436 | GLM 5 |
| LMArena Text: Polish | 1324 | 1434 | GLM 5 |
| LMArena Text: multi-turn | 1338 | 1456 | GLM 5 |
| LMArena Text: longer queries | 1334 | 1446 | GLM 5 |
| LMArena Text: writing and language | 1328 | 1437 | GLM 5 |
| LMArena Text: entertainment and media | 1306 | 1426 | GLM 5 |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | GLM 5 | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GLM 5 costs less
Repository review
50K input + 3K output tokens
GLM 5 costs less
Agent loop
200K input + 10K output tokens
GLM 5 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Cohere Command A | Z.ai GLM 5 |
|---|---|---|
| Maker | Cohere | Z.ai |
| Weights | Open weights | Open weights |
| Released | March 13, 2025 | February 11, 2026 |
| Context window | 256K | 205K |
| Input $/Mtok | $2.50 | $1.00 |
| Output $/Mtok | $10.00 | $3.20 |
| Coding index | Not benchmarked | 41.7 |
| Independent measurements behind it | 1 | 3 |
| Cost per solved task | $2.69 | $0.63 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command A better than GLM 5 for coding?
Only GLM 5 has a coding score, 41.7 on the Vibe Coding Index; Command A has none, so the two cannot be ranked on coding. Pick Command A for a larger context window (256K against 205K tokens) and work measured by CaseLaw (Vals AI) or IFBench, where it leads.
Which is cheaper, Command A or GLM 5?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; GLM 5 costs $1.00 and $3.20. Per solved SWE-bench Verified task, Command A costs $2.69 and GLM 5 $0.63.
Which has the bigger context window, Command A or GLM 5?
Command A, with 256K tokens against 205K.
How many benchmarks is the Command A vs GLM 5 comparison based on?
46 benchmarks have a result for both models as of October 22, 2026. Command A's coding index rests on 1 independent benchmark and GLM 5's on 3. Where only one model has a result, the page says so instead of naming a winner.